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Platzhalter für Bild, Bild auf Titelfolie hinter das Logo einsetzen Institute of Agricultural Maschinery and Fluid Power Precise relative positioning in

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Precise relative positioning in machine swarms

Dipl.-Ing. Jan SchattenbergProf. Dr.-Ing. T. LangDipl.-Ing. M. BeckerDipl.-Ing. S. BatzdorferDr.-Ing. U. BestmannProf. Dr.-Ing. P. HeckerDr.-Ing. F. Andert3rd International Conference on Machine Control GuidancePlatzhalter fr Bild, Bild auf Titelfolie hinter das Logo einsetzen Institute of Agricultural Maschinery and Fluid PowerInstitute of Agricultural Machinery and Fluid Power29.03.2012 | Dipl.-Ing. Jan Schattenberg | Precise relative positioning in machine swarms | Slide Nr.

Dear ladies and Gentleman, I am very pleased to welcome you to the last session of the MCG2012 and to my presentation named Precise relative positioning in machine swarms In the next 25 Minutes I will give you an overview about what the title is standing for and I will give you an introduction into the project and the project partners. But, firstly, lets take a look at the outline of my presentation.1Motivation and Introduction

IMU/GNSS and Vision integration

Swarm Positioning

Mobile Ad-Hoc Communication

First Results

Conclusion and OutlookOutline

Institute of Agricultural Machinery and Fluid Power29.03.2012 | Dipl.-Ing. Jan Schattenberg | Precise relative positioning in machine swarms | Slide Nr.

2

Urban and alpine scenarioShadowing of GNSS signals, degradingGNSS signals, multipath effects Guidance with the help of known landmarks is limitedFast search with high accuracy positioning

Integration of IMU/GNSS including Failure Detection and Exclusion MethodsVision-aided relative localizationSwarm Positioning using GNSS raw data exchangeMobile Ad-hoc communication for GNSS raw data exchange

Motivation

Institute of Agricultural Machinery and Fluid Power29.03.2012 | Dipl.-Ing. Jan Schattenberg | Precise relative positioning in machine swarms | Slide Nr.

3Two scenarios an urban an and alpine scenario defines the requirements for the techniques for relative positioning. Both deals with search and rescue tasks of victims, e.g. after a snowslide or an earthquake. First step is a fast exploration (time of live less then ~20 minutes) of a wide area with several rovers in a line similar to known search and rescue procedures of snow slide victims. Second step is to change after an approximate detection of the victim from the line constellation to a circular geometry which provides a more accurate localization of the victim.Urban scenarios are much more complex than the alpine scenarios and have multitude boundary conditions in respect of accuracy of Positioning.

GNSS signals will be shadowed by buildings, so there might be a lost of the line of sight between the UxV (Receiver) and a satellite. (picture) Also a degrading of signals will be expected, which needs to be detected (FDE).

Another known effect in urban canyon is multipath (red line)

All this effects result in a lower accuracy of (absolute) positioning

So in the urban scenario a collision avoidance is important because of nearby operation of the rover and short distance (for example) to buildings. To avoid such collisions a relative position is needed.

Based on the scenarios, four main work packages have been identified to improve the positioning :

Introduction NExt UAVJoint research project NExt UAV

Institute of Flight GuidanceInstitute of Agricultural Machinery and Fluid PowerInstitute of Flight Systems

Funded by

FKZ 50NA1002 and 50NA1003

Institute of Agricultural Machinery and Fluid Power29.03.2012 | Dipl.-Ing. Jan Schattenberg | Precise relative positioning in machine swarms | Slide Nr.

The techniques and data shown in this presentation and published in the paper are developed within the NExt UAV Project Which is a joint research project funded by the Bundesministerium fr Wirtschaft und Technologie (BMWi) administered by the Space Administration of the German Aerospace Center (DLR) in Bonn.

NExt UAV is a german acronym which can be translated as Navigation and Exploration with UAV (unmaned aerial vehicle) at low altitude in disaster scenarios. in german: Navigation zur Exploration mit tieffliegenden UAV in KatatstrophenszenarienThree partners.IFF: The Institute of Flight Guidance focus on GNSS, inertial and integrated navigation and on the testbed used for the experiments -> QuadrocopterDLR: Institute of flight systems German Aerospace Center (DLR). Motion estimation by video system optical flow They developed a miniature (small) helicopter named ARTIS provided with sensors for optical navigation. ILF: Focus on radio-based data exchange within the swarm and on unmanned ground vehicle (UGV)4Motivation and Introduction

IMU/GNSS and Vision integration

Swarm Positioning

Mobile Ad-Hoc Communication

First Results

Conclusion and OutlookOutline

Institute of Agricultural Machinery and Fluid Power29.03.2012 | Dipl.-Ing. Jan Schattenberg | Precise relative positioning in machine swarms | Slide Nr.

5

IMU/GNSS and Vision integration - FDESystem architectureMain filter processes all measurements (N)Each subfilter processes (N-i) measurements (i = 1N-1)Institute of Agricultural Machinery and Fluid Power29.03.2012 | Dipl.-Ing. Jan Schattenberg | Precise relative positioning in machine swarms | Slide Nr.

6shown in the motivation slide a Detection of Degenerated GNSS signals is needed to guarantee a high position accuracy. Therefore a Failure Detection and Exclusion (FDE) will be implemented by the institute of flight guidance systematical architecture is pictured in this slide

The Inputs are IMU and GNSS data which are proceeded within a filterbank. Components of the filterbank are the Main filter processing all received measurements and Subfilters processing N-1 measurements. N is the number of received measurements. where one measurement is always excluded from the processing

After going thru Statistical with plausibility checks and further tests a fault detection is attached.

If there are no failure of the main filter a position/velocity can be computed.

If there is a failure and this is detected a identification of fault free sub filter is realized and this fault free subfilter (EINER) is used for computation of position /velocity.

After that the filter bank will resetted.

The so called main filter uses all measurements, the inertial and all GPS pseudorange measurements. Each one of the subfilters neglects one GPS measurement. If a failure occurs, the faultfree subfilter could be detected by applying statistical tests. This one could then be used to supply positioning information and to reset the whole filterbank for further processing.IMU/GNSS and Vision integration Vision based localization

Institute of Agricultural Machinery and Fluid Power29.03.2012 | Dipl.-Ing. Jan Schattenberg | Precise relative positioning in machine swarms | Slide Nr.

Feature TrackingImage Processing

Features are tracked within the picture sequence to calculate the motion of the robot. 2D only relative, 3D absolute.

Basis for image-based localization are modern algorithms that calculate the cameras motion from image sequences in real-time. This is performed by identifying and tracking significant features (e.g. edges and corners with high contrast) over time, known as optical flow. With the characteristics of these feature movements, the ego-motion of the camera can be calculated. With that, the localization filter bank avails an additional sensor to compensate the disadvantages of GNSS and IMU. Actual research investigates techniques and algorithms for an overall integration of camera systems into a reliable and robust navigation system.

Since localization should be applicable to unknown environments, these features are not matched with prior knowledge from environmental maps. With that, optical flow gives only information about the relative orientation between two camera frames. Due to that, an image processing system measures only relative movements like an inertial system which means that similar issues like error accumulation must be considered. In addition to that, the relative movements are scale-invariant which means that there are only five of six degrees of freedom measured, with no information about the absolute size of movement. With two or more calibrated cameras, absolute movements can be measured, though, up to a certain distance depending on the camera baselines. The stereo camera system used within this project allows the measurement of distances between approximately 10 m and 40 m, which means that images of objects in this distance field will give full movement information in six degrees of freedom.

7IMU/GNSS and Vision integration - Coupling VisionFeature PixelPosition

Feature WorldPosition

Predicted FeatureWorld Position

Predicted PixelPosition

MonitoringTight coupledIMUGNSSCorrection

Prediction

INS/GNSSInstitute of Agricultural Machinery and Fluid Power29.03.2012 | Dipl.-Ing. Jan Schattenberg | Precise relative positioning in machine swarms | Slide Nr.

EKF: Extended Kalman Filter - > non linear system

In addition to the integration of an optical flow sensor into the navigation filter, research investigations are going to improve the image processing performance and robustness by re-coupling the navigation prediction into the optical flow measurements as depicted in Figure 6. This is done because classical optical flow and image-based relative localization only depend on the image quality. In this new approach, the navigation prediction helps to find an initial solution for the image movement and to eventually filter or correct bad optical flow vectors. Additionally, a p